Agentic
Not comparable- GPT-5.6 Terra
- 60.6
- Supported · #17/154
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 9 vs 9 public rows
- Reading
- Not comparable
GPT-5.6 Terra vs MiMo-V2.6-Pro
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 21, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
MiMo-V2.6-Pro
MiMo-V2.6-Pro has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MiMo-V2.6-Pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MiMo-V2.6-Pro is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
68.2GPT-5.6 Terra—MiMo-V2.6-Pro
Not comparable · BenchAlign
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
30 GPT-5.6 Terra only
3 Shared
9 MiMo-V2.6-Pro only
- Shared results
- 3
- GPT-5.6 Terra only
- 30
- MiMo-V2.6-Pro only
- 9
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- GPT-5.6 Terra
- 68.2
- Supported · #7/156
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 9 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Terra
- 63.7
- #13/19
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Terra
- 77.2
- #15/49
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Terra
- 69.7
- Supported · #14/186
- MiMo-V2.6-Pro
- Not ranked
- Basis
- BenchAlign lane · 8 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Terra
- Not ranked
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Terra
- 85.7
- #35/124
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Terra
- 96.8
- Unranked · 3 rankable rows
- MiMo-V2.6-Pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Terra | MiMo-V2.6-Pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.6Supported · #17/154 | Not ranked | Not comparableBenchAlign lane · 9 vs 9 public rows | Not comparable |
| Coding | 68.2Supported · #7/156 | Not ranked | Not comparableBenchAlign lane · 9 vs 3 public rows | Not comparable |
| Reasoning | 63.7#13/19 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 77.2#15/49 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 69.7Supported · #14/186 | Not ranked | Not comparableBenchAlign lane · 8 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 85.7#35/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-5.6 Terra
- $0.008
- Fits in one request
- MiMo-V2.6-Pro
- $0.00087
- Fits in one request
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Terra
- $0.136
- Fits in one request
- MiMo-V2.6-Pro
- $0.02436
- Fits in one request
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Terra
- $0.2
- Fits in one request
- MiMo-V2.6-Pro
- $0.01812
- Fits in one request
MiMo-V2.6-Pro has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 TerraMiMo-V2.6-Pro
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Terra
1.05M
OpenAI model catalogMiMo-V2.6-Pro
API model ID
GPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogMiMo-V2.6-Pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Terra
$0.2 per 1M cached input tokens
OpenAI pricingMiMo-V2.6-Pro
$0.0036 per 1M cached input tokens
Xiaomi MiMo-V2.6 launchDocumented inputs
GPT-5.6 Terra
text, image
OpenAI model catalogMiMo-V2.6-Pro
Not sourced
Documented outputs
GPT-5.6 Terra
MiMo-V2.6-Pro
Not sourced
Provider availability
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogMiMo-V2.6-Pro
Not sourced
Reasoning profile
GPT-5.6 Terra
Reasoning
MiMo-V2.6-Pro
Reasoning
Weight access
GPT-5.6 Terra
Proprietary
MiMo-V2.6-Pro
Open Weight
License
GPT-5.6 Terra
Proprietary
MiMo-V2.6-Pro
Open Weight
Release date
GPT-5.6 Terra
2026-07-09
MiMo-V2.6-Pro
2026-09-22
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.136 vs $0.02436. Cache-heavy agent loop: $0.2 vs $0.01812.
- Context tradeoff
- GPT-5.6 Terra has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Terra20.8%SourceMiMo-V2.6-Pro—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceMiMo-V2.6-Pro—Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%SourceMiMo-V2.6-Pro—Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%SourceMiMo-V2.6-Pro—Not directly comparable
CyberGym
GPT-5.6 Terra81.8%SourceMiMo-V2.6-Pro94.0%SourceMiMo-V2.6-Pro leads this result
ExploitGym
GPT-5.6 Terra23.2%SourceMiMo-V2.6-Pro17.8%SourceGPT-5.6 Terra leads this result
Toolathlon
GPT-5.6 Terra53.1%SourceMiMo-V2.6-Pro—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra77.5%SourceMiMo-V2.6-Pro—Not directly comparable
ApprenticeBench
GPT-5.6 Terra16%SourceMiMo-V2.6-Pro—Not directly comparable
Toolathlon-Verified
GPT-5.6 Terra—MiMo-V2.6-Pro76.9%SourceNot directly comparable
AutomationBench
GPT-5.6 Terra—MiMo-V2.6-Pro53.1%SourceNot directly comparable
Agents' Last Exam
GPT-5.6 Terra—MiMo-V2.6-Pro31.6%SourceNot directly comparable
Terminal-Bench 4.0
GPT-5.6 Terra—MiMo-V2.6-Pro34.90%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra—MiMo-V2.6-Pro89.9%SourceNot directly comparable
OSWorld-Verified
GPT-5.6 Terra—MiMo-V2.6-Pro82%SourceNot directly comparable
JobBench
GPT-5.6 Terra—MiMo-V2.6-Pro62.0%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Terra63.4%SourceMiMo-V2.6-Pro—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra87.4%SourceMiMo-V2.6-Pro—Not directly comparable
DeepSWE
GPT-5.6 Terra69.6%SourceMiMo-V2.6-Pro71.9%SourceMiMo-V2.6-Pro leads this result
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%SourceMiMo-V2.6-Pro—Not directly comparable
cursorBench32
GPT-5.6 Terra64.9%SourceMiMo-V2.6-Pro—Not directly comparable
VulcanBench v3
GPT-5.6 Terra87.0%SourceMiMo-V2.6-Pro—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Terra85.9%SourceMiMo-V2.6-Pro—Not directly comparable
SWE-bench (Vals)
GPT-5.6 Terra95.4%SourceMiMo-V2.6-Pro—Not directly comparable
cursorBench40
GPT-5.6 Terra41.3%SourceMiMo-V2.6-Pro—Not directly comparable
ProgramBench
GPT-5.6 Terra—MiMo-V2.6-Pro26.5%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.6 Terra—MiMo-V2.6-Pro89.9%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Terra92.9%SourceMiMo-V2.6-Pro—Not directly comparable
GPQA-D
GPT-5.6 Terra92.9%SourceMiMo-V2.6-Pro—Not directly comparable
HLE-Verified
GPT-5.6 Terra51.1%SourceMiMo-V2.6-Pro—Not directly comparable
LABBench2
GPT-5.6 Terra81.2%SourceMiMo-V2.6-Pro—Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%SourceMiMo-V2.6-Pro—Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%SourceMiMo-V2.6-Pro—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Terra90.9%SourceMiMo-V2.6-Pro—Not directly comparable
MMLU-Pro (Vals)
GPT-5.6 Terra86.7%SourceMiMo-V2.6-Pro—Not directly comparable
Math
Questions
Which is better, GPT-5.6 Terra or MiMo-V2.6-Pro?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Terra or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.6 Terra or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.6 Terra or MiMo-V2.6-Pro?
For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.00087 on MiMo-V2.6-Pro; repository review costs $0.136 and $0.02436; the cache-heavy agent loop costs $0.2 and $0.01812. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Terra or MiMo-V2.6-Pro?
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated September 21, 2026
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